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"tests/pipelines/test_pipelines_audio_classification.py" did not exist on "c9184a2e0340608c7f5630107b8afda6f0544f9a"
Commit 4b7a267a authored by Paul's avatar Paul
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src/include/migraphx/op/softmax.hpp 0 → 100644
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_SOFTMAX_HPP
#define MIGRAPHX_GUARD_OPERATORS_SOFTMAX_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct softmax
{
std::string name() const { return "softmax"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1).only_dims(4);
return inputs.at(0);
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/squeeze.hpp 0 → 100644
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_SQUEEZE_HPP
#define MIGRAPHX_GUARD_OPERATORS_SQUEEZE_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct squeeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "squeeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
if(std::any_of(
axes.begin(), axes.end(), [&](auto axis) { return input_shape.lens()[axis] != 1; }))
{
MIGRAPHX_THROW("squeeze axis dimension should be equal to 1");
}
std::vector<std::size_t> new_lens;
if(axes.empty())
{
std::copy_if(old_lens.begin(),
old_lens.end(),
std::back_inserter(new_lens),
[](auto len) { return len != 1; });
}
else
{
for(std::size_t i = 0; i < old_lens.size(); i++)
{
if(std::find(axes.begin(), axes.end(), i) == axes.end())
{
new_lens.push_back(old_lens[i]);
}
}
}
if(new_lens.empty())
{
return shape{type};
}
else
{
return shape{type, new_lens};
}
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/sub.hpp 0 → 100644
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_SUB_HPP
#define MIGRAPHX_GUARD_OPERATORS_SUB_HPP
#include <array>
#include <migraphx/op/binary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct sub : binary<sub>
{
auto apply() const
{
return [](auto x, auto y) { return x - y; };
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/tan.hpp 0 → 100644
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#ifndef MIGRAPHX_GUARD_OPERATORS_TAN_HPP
#define MIGRAPHX_GUARD_OPERATORS_TAN_HPP
#include <array>
#include <migraphx/op/unary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct tan : unary
{
std::string name() const { return "tan"; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/tanh.hpp 0 → 100644
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_TANH_HPP
#define MIGRAPHX_GUARD_OPERATORS_TANH_HPP
#include <array>
#include <migraphx/op/unary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct tanh : unary
{
std::string name() const { return "tanh"; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/transpose.hpp 0 → 100644
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_TRANSPOSE_HPP
#define MIGRAPHX_GUARD_OPERATORS_TRANSPOSE_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct transpose
{
std::vector<int64_t> dims;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dims, "dims"));
}
std::string name() const { return "transpose"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto input = inputs.at(0);
auto input_lens = input.lens();
auto input_strides = input.strides();
auto t = input.type();
if(dims.size() != input_lens.size())
{
MIGRAPHX_THROW("Permutation has wrong number of axes");
}
std::vector<int64_t> axes(dims.size());
std::iota(axes.begin(), axes.end(), 0);
if(!std::is_permutation(axes.begin(), axes.end(), dims.begin()))
{
MIGRAPHX_THROW("Invalid permutation");
}
std::vector<size_t> output_lens(input_lens.size());
std::vector<size_t> output_strides(input_lens.size());
for(std::size_t i = 0; i < output_lens.size(); i++)
{
output_lens[i] = input_lens[dims[i]];
output_strides[i] = input_strides[dims[i]];
}
return {t, output_lens, output_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/unary.hpp 0 → 100644
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_UNARY_HPP
#define MIGRAPHX_GUARD_OPERATORS_UNARY_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct unary
{
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1);
return inputs.at(0);
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/unsqueeze.hpp 0 → 100644
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_UNSQUEEZE_HPP
#define MIGRAPHX_GUARD_OPERATORS_UNSQUEEZE_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct unsqueeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "unsqueeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
std::size_t new_size = old_lens.size() + axes.size();
std::vector<std::size_t> new_lens(new_size);
std::size_t p = 0;
for(std::size_t i = 0; i < new_size; i++)
{
if(std::find(axes.begin(), axes.end(), i) != axes.end())
{
new_lens[i] = 1;
}
else
{
new_lens[i] = old_lens[p++];
}
}
return shape{type, new_lens};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/operators.hpp
View file @ 4b7a267a
#ifndef MIGRAPHX_GUARD_OPERATORS_HPP
#define MIGRAPHX_GUARD_OPERATORS_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/type_name.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
enum padding_mode_t
{
default_, // NOLINT
same,
valid
};
struct not_computable
{
argument compute(const shape&, const std::vector<argument>&) const
{
MIGRAPHX_THROW("not computable");
}
};
struct batch_norm_inference
{
float epsilon = 1.0e-6f;
float momentum = 0.9f;
std::string name() const { return "batch_norm_inference"; }
enum bn_infer_mode_t
{
per_activation,
spatial,
};
bn_infer_mode_t bn_mode = spatial;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(
f(self.epsilon, "epsilon"), f(self.momentum, "momentum"), f(self.bn_mode, "bn_mode"));
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(5);
check_shapes{inputs.data(), inputs.data() + 1, *this}.only_dims(4);
check_shapes{inputs.data() + 1, inputs.data() + inputs.size(), *this}.same_shape().elements(
inputs.front().lens()[1]);
return inputs.front();
}
};
struct lrn
{
float alpha = 0.0001;
float beta = 0.75;
float bias = 1.0;
int size = 1;
std::string name() const { return "lrn"; }
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"),
f(self.beta, "beta"),
f(self.bias, "bias"),
f(self.size, "size"));
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
return inputs.front();
}
};
struct convolution
{
std::array<std::size_t, 2> padding = {{0, 0}};
std::array<std::size_t, 2> stride = {{1, 1}};
std::array<std::size_t, 2> dilation = {{1, 1}};
padding_mode_t padding_mode = default_;
int group = 1;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.padding, "padding"),
f(self.stride, "stride"),
f(self.dilation, "dilation"),
f(self.padding_mode, "padding_mode"),
f(self.group, "group"));
}
std::string name() const { return "convolution"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(2).same_type().same_ndims().only_dims(4);
const shape& input = inputs.at(0);
const shape& weights = inputs.at(1);
auto t = input.type();
if(padding_mode == default_)
{
return {t,
{
input.lens()[0],
weights.lens()[0],
std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[2] - (1 + dilation[0] * (weights.lens()[2] - 1)) +
2 * padding[0]) /
stride[0] +
1)),
std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[3] - (1 + dilation[1] * (weights.lens()[3] - 1)) +
2 * padding[1]) /
stride[1] +
1)),
}};
}
else if(padding_mode == same)
{
return {t,
{input.lens()[0],
weights.lens()[0],
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
}
else if(padding_mode == valid)
{
return {
t,
{input.lens()[0],
weights.lens()[0],
static_cast<std::size_t>(std::ceil(
static_cast<double>(input.lens()[2] - weights.lens()[2] + 1) / stride[0])),
static_cast<std::size_t>(std::ceil(
static_cast<double>(input.lens()[3] - weights.lens()[3] + 1) / stride[1]))}};
}
else
{
MIGRAPHX_THROW("Invalid padding mode");
}
}
};
struct im2col
{
std::array<std::size_t, 2> padding = {{0, 0}};
std::array<std::size_t, 2> stride = {{1, 1}};
std::array<std::size_t, 2> dilation = {{1, 1}};
padding_mode_t padding_mode = default_;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.padding, "padding"),
f(self.stride, "stride"),
f(self.dilation, "dilation"),
f(self.padding_mode, "padding_mode"));
}
std::string name() const { return "im2col"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input = inputs[0];
auto weights = inputs[1];
auto batch_size = input.lens()[0];
auto input_channels = weights.lens()[1];
auto kernel_height = weights.lens()[2];
auto kernel_width = weights.lens()[3];
check_shapes{inputs, *this}.has(2);
if(batch_size != 1)
MIGRAPHX_THROW("im2col only support batch_size 1");
auto output_height = std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[2] - (1 + dilation[0] * (kernel_height - 1)) + 2 * padding[0]) /
stride[0] +
1));
auto output_width = std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[3] - (1 + dilation[1] * (kernel_width - 1)) + 2 * padding[1]) /
stride[1] +
1));
auto channels_col = kernel_height * kernel_width * input_channels;
return {input.type(), {output_height * output_width, channels_col}};
}
};
struct pooling
{
std::string mode = "average";
std::array<std::size_t, 2> padding = {{0, 0}};
std::array<std::size_t, 2> stride = {{1, 1}};
std::array<std::size_t, 2> lengths = {{1, 1}};
padding_mode_t padding_mode = default_;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.mode, "mode"),
f(self.padding, "padding"),
f(self.padding, "padding_mode"),
f(self.stride, "stride"),
f(self.lengths, "lengths"));
}
std::string name() const { return "pooling"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1).only_dims(4);
const shape& input = inputs.at(0);
auto t = input.type();
assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
if(padding_mode == default_)
{
return {
t,
{
input.lens()[0],
input.lens()[1],
std::size_t(std::max<std::ptrdiff_t>(
1,
std::ptrdiff_t(std::floor((input.lens()[2] + 2 * padding[0] - lengths[0]) /
static_cast<float>(stride[0]))) +
1)),
std::size_t(std::max<std::ptrdiff_t>(
1,
std::ptrdiff_t(std::floor((input.lens()[3] + 2 * padding[1] - lengths[1]) /
static_cast<float>(stride[1]))) +
1)),
}};
}
else if(padding_mode == same)
{
return {t,
{input.lens()[0],
input.lens()[1],
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
}
else if(padding_mode == valid)
{
return {t,
{
input.lens()[0],
input.lens()[1],
std::size_t(std::max<std::ptrdiff_t>(
1,
std::ptrdiff_t(std::floor((input.lens()[2] - lengths[0]) /
static_cast<float>(stride[0]))) +
1)),
std::size_t(std::max<std::ptrdiff_t>(
1,
std::ptrdiff_t(std::floor((input.lens()[3] - lengths[1]) /
static_cast<float>(stride[1]))) +
1)),
}};
}
else
{
MIGRAPHX_THROW("Invalid padding mode");
}
}
};
struct leaky_relu
{
std::string name() const { return "leaky_relu"; }
float alpha;
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
return inputs.front();
}
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"));
}
};
struct elu
{
std::string name() const { return "elu"; }
float alpha;
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
return inputs.front();
}
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"));
}
};
struct transpose
{
std::vector<int64_t> dims;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dims, "dims"));
}
std::string name() const { return "transpose"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto input = inputs.at(0);
auto input_lens = input.lens();
auto input_strides = input.strides();
auto t = input.type();
if(dims.size() != input_lens.size())
{
MIGRAPHX_THROW("Permutation has wrong number of axes");
}
std::vector<int64_t> axes(dims.size());
std::iota(axes.begin(), axes.end(), 0);
if(!std::is_permutation(axes.begin(), axes.end(), dims.begin()))
{
MIGRAPHX_THROW("Invalid permutation");
}
std::vector<size_t> output_lens(input_lens.size());
std::vector<size_t> output_strides(input_lens.size());
for(std::size_t i = 0; i < output_lens.size(); i++)
{
output_lens[i] = input_lens[dims[i]];
output_strides[i] = input_strides[dims[i]];
}
return {t, output_lens, output_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
/// The contiguous operator takes a non-standard input tensor and returns
/// the same tensor but in standard form. For example, if input tensor A which has lens = (4,5)
/// is first transposed, i.e. lens = (5,4), this tensor's data layout remained the same
/// during the transpose operation; only it's shape lengths and strides were changed.
/// This leaves the tensor in a non-standard form. The contiguous operator copies the
/// underlying data such that resulting tensor is returned to a standard form.
struct contiguous
{
std::string name() const { return "contiguous"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto lens = inputs.at(0).lens();
auto t = inputs.at(0).type();
return {t, lens};
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
assert(output_shape.standard());
argument result{output_shape};
visit_all(result, args[0])([&](auto output, auto input) {
shape_for_each(output.get_shape(), [&](const auto& idx) {
output(idx.begin(), idx.end()) = input(idx.begin(), idx.end());
});
});
return result;
}
};
struct concat
{
std::size_t axis = 0;
std::string name() const { return "concat"; }
std::vector<std::size_t> compute_offsets(const shape& output_shape,
const std::vector<argument>& args) const
{
std::vector<std::size_t> offsets;
std::vector<std::size_t> offset(args[0].get_shape().lens().size(), 0);
offset[axis] = 0;
for(const auto& arg : args)
{
offsets.push_back(output_shape.index(offset));
offset[axis] += arg.get_shape().lens()[axis];
}
return offsets;
}
shape compute_shape(std::vector<shape> inputs) const
{
if(inputs.empty())
{
MIGRAPHX_THROW("Number of input tensors should exceed 0");
}
const auto& first_shape_lens = inputs.front().lens();
const auto& type = inputs.front().type();
for(std::size_t l = 0; l < first_shape_lens.size(); l++)
{
if(l != axis)
{
if(!std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
return s.lens()[l] == first_shape_lens[l];
}))
{
MIGRAPHX_THROW("Non-axis dimensions should match");
}
}
}
std::size_t new_dim_axis = 0;
for(const auto& input : inputs)
{
const auto& lens = input.lens();
new_dim_axis += lens[axis];
}
std::vector<std::size_t> new_lens;
std::copy(first_shape_lens.begin(), first_shape_lens.end(), std::back_inserter(new_lens));
new_lens[axis] = new_dim_axis;
return {type, new_lens};
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
std::vector<std::size_t> coffsets = compute_offsets(output_shape, args);
for(std::size_t l = 0; l < args.size(); l++)
{
auto argl = args[l];
std::size_t nelements = argl.get_shape().elements();
visit_all(result, argl)([&](auto output, auto input) {
auto slice_shape =
shape{output_shape.type(), input.get_shape().lens(), output_shape.strides()};
auto slice = make_view(slice_shape, output.data() + coffsets[l]);
// cppcheck-suppress useStlAlgorithm
for(std::size_t i = 0; i < nelements; i++)
{
slice[i] = input[i];
}
});
}
return result;
}
};
struct slice
{
std::vector<int64_t> axes;
std::vector<int64_t> starts;
std::vector<int64_t> ends;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"), f(self.starts, "starts"), f(self.ends, "ends"));
}
std::string name() const { return "slice"; }
auto fix_index(const std::vector<std::size_t>& lens, std::size_t axis, int64_t index) const
{
int64_t r = std::min(index, static_cast<int64_t>(lens[axis]));
if(r < 0)
r += lens[axis];
return std::size_t(r);
}
auto compute_offset(const shape& s) const
{
const std::vector<std::size_t>& lens = s.lens();
const std::vector<std::size_t>& strides = s.strides();
auto offset = 0;
if(!axes.empty())
{
for(std::size_t i = 0; i < axes.size(); i++)
{
auto axis = axes[i];
offset += fix_index(lens, axis, starts[i]) * strides[axis];
}
}
else
{
for(std::size_t axis = 0; axis < lens.size(); axis++)
{
offset += fix_index(lens, axis, starts[axis]) * strides[axis];
}
}
return offset;
}
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto t = input_shape.type();
const auto& old_lens = input_shape.lens();
const auto& old_strides = input_shape.strides();
if(starts.size() != axes.size() || axes.size() != ends.size())
{
MIGRAPHX_THROW("inconsistent sizes");
}
std::vector<std::size_t> new_lens = old_lens;
for(std::size_t i = 0; i < axes.size(); i++)
{
auto axis = axes[i];
new_lens[axis] =
fix_index(old_lens, axis, ends[i]) - fix_index(old_lens, axis, starts[i]);
}
return shape{t, new_lens, old_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
auto input = args[0];
auto offset = compute_offset(input.get_shape()) * output_shape.type_size();
return {std::move(output_shape), [=] { return input.data() + offset; }};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct squeeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "squeeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
if(std::any_of(
axes.begin(), axes.end(), [&](auto axis) { return input_shape.lens()[axis] != 1; }))
{
MIGRAPHX_THROW("squeeze axis dimension should be equal to 1");
}
std::vector<std::size_t> new_lens;
if(axes.empty())
{
std::copy_if(old_lens.begin(),
old_lens.end(),
std::back_inserter(new_lens),
[](auto len) { return len != 1; });
}
else
{
for(std::size_t i = 0; i < old_lens.size(); i++)
{
if(std::find(axes.begin(), axes.end(), i) == axes.end())
{
new_lens.push_back(old_lens[i]);
}
}
}
return shape{type, new_lens};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct unsqueeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "unsqueeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
std::size_t new_size = old_lens.size() + axes.size();
std::vector<std::size_t> new_lens(new_size);
std::size_t p = 0;
for(std::size_t i = 0; i < new_size; i++)
{
if(std::find(axes.begin(), axes.end(), i) != axes.end())
{
new_lens[i] = 1;
}
else
{
new_lens[i] = old_lens[p++];
}
}
return shape{type, new_lens};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct reshape
{
std::vector<int64_t> dims;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dims, "dims"));
}
std::string name() const { return "reshape"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto&& idims = inputs.front().lens();
std::vector<std::size_t> rdims(dims.begin(), dims.end());
auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
if(n_neg_dims > 1)
MIGRAPHX_THROW("Dimensions for reshape can only have one -1 dim");
for(std::size_t i = 0; i < dims.size(); i++)
{
if(dims[i] == 0)
rdims[i] = idims[i];
// since rdims using size_t type, -1 is the max value
// is size_t that cause later compuation incorrect
if(dims[i] == -1)
rdims[i] = 1;
}
if(n_neg_dims > 0)
{
size_t missing_dim =
inputs.front().elements() /
std::accumulate(rdims.begin(), rdims.end(), 1, std::multiplies<int64_t>());
for(std::size_t i = 0; i < rdims.size(); i++)
{
if(dims[i] == -1)
rdims[i] = missing_dim;
}
}
shape s{inputs.front().type(), rdims};
if(s.elements() != inputs.front().elements())
MIGRAPHX_THROW("Wrong number of elements for reshape");
return s;
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct pad
{
std::vector<int64_t> pads;
float value = 0.0f;
enum pad_op_mode_t
{
constant_pad,
reflect_pad,
edge_pad
};
pad_op_mode_t mode = constant_pad;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.mode, "mode"), f(self.pads, "pads"), f(self.value, "value"));
}
std::string name() const { return "pad"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto&& idims = inputs.front().lens();
std::vector<std::size_t> rdims(idims.begin(), idims.end());
std::size_t num_dims = rdims.size();
for(std::size_t i = 0; i < num_dims; i++)
{
rdims[i] += pads[i] + pads[i + num_dims];
}
shape s{inputs.front().type(), rdims};
return s;
}
};
struct as_shape
{
shape s;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.s, "shape"));
}
std::string name() const { return "as_shape"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(1).standard();
assert(inputs.front().elements() == s.elements());
return s;
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct gather
{
int axis = 0;
std::string name() const { return "gather"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(2);
auto lens = inputs[0].lens();
int n_dim = static_cast<int>(lens.size());
if(axis >= n_dim || axis < -n_dim)
{
MIGRAPHX_THROW("Gather: axis is out of range.");
}
// negative axis means counting dimensions from back
int axis_index = (axis < 0) ? (n_dim + axis) : axis;
auto type = inputs[0].type();
lens.erase(lens.begin() + axis_index);
if(!inputs[1].scalar())
{
auto ind_lens = inputs[1].lens();
lens.insert(lens.begin() + axis_index, ind_lens.begin(), ind_lens.end());
}
// for scalar output
if(lens.empty())
{
return {type};
}
return {type, lens};
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
// negative axis means counting dimensions from back
int axis_index =
(axis < 0) ? static_cast<int>(args[0].get_shape().lens().size() + axis) : axis;
// max dimension in axis
visit_all(result, args[0])([&](auto output, auto data) {
args[1].visit([&](auto indices) {
if(output_shape.scalar())
{
output[0] = data[indices.front()];
}
else
{
auto out_lens = data.get_shape().lens();
out_lens[axis_index] = indices.get_shape().elements();
migraphx::shape out_comp_shape{data.get_shape().type(), out_lens};
shape_for_each(out_comp_shape, [&](const auto& out_idx) {
auto data_idx = out_idx;
data_idx[axis_index] = indices[data_idx[axis_index]];
output[out_comp_shape.index(out_idx.begin(), out_idx.end())] =
data(data_idx.begin(), data_idx.end());
});
}
});
});
return result;
}
};
struct dot
{
float alpha = 1.0;
float beta = 0.0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"), f(self.beta, "beta"));
}
std::string name() const { return "dot"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(2).same_type();
const shape& a = inputs.at(0);
const shape& b = inputs.at(1);
auto t = a.type();
// according to the specification of the numpy.matmul()
// inputs with the shape dims more than 2 are acceptable
// as long as dim values are the same in the two inputs
if(!std::equal(a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2))
{
MIGRAPHX_THROW("DOT: dim values mismatch");
}
std::size_t dim_0 = a.lens().size() - 2;
std::size_t dim_1 = a.lens().size() - 1;
if(a.lens()[dim_1] != b.lens()[dim_0])
MIGRAPHX_THROW("Inner dimensions do not match: {" + to_string_range(a.lens()) +
"} x {" + to_string_range(b.lens()) + "}");
auto out_lens = a.lens();
out_lens[dim_1] = b.lens()[dim_1];
return {t, out_lens};
}
};
struct unary
{
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1);
return inputs.at(0);
}
};
struct identity
{
std::string name() const { return "identity"; }
shape compute_shape(std::vector<shape> inputs) const { return inputs.at(0); }
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct abs : unary
{
std::string name() const { return "abs"; }
};
struct exp : unary
{
std::string name() const { return "exp"; }
};
struct log : unary
{
std::string name() const { return "log"; }
};
struct sin : unary
{
std::string name() const { return "sin"; }
};
struct cos : unary
{
std::string name() const { return "cos"; }
};
struct tan : unary
{
std::string name() const { return "tan"; }
};
struct asin : unary
{
std::string name() const { return "asin"; }
};
struct acos : unary
{
std::string name() const { return "acos"; }
};
struct atan : unary
{
std::string name() const { return "atan"; }
};
struct sinh : unary
{
std::string name() const { return "sinh"; }
};
struct cosh : unary
{
std::string name() const { return "cosh"; }
};
struct tanh : unary
{
std::string name() const { return "tanh"; }
};
struct sigmoid : unary
{
std::string name() const { return "sigmoid"; }
};
struct neg : unary
{
std::string name() const { return "neg"; }
};
struct relu : unary
{
std::string name() const { return "relu"; }
};
struct softmax
{
std::string name() const { return "softmax"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1).only_dims(4);
return inputs.at(0);
}
};
struct logsoftmax
{
int axis = 1;
std::string name() const { return "logsoftmax"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1);
if(axis < 0 || axis > inputs[0].lens().size())
{
MIGRAPHX_THROW("LogSoftMax: input axis value " + std::to_string(axis) +
" is out of range");
}
return inputs.at(0);
}
};
struct flatten
{
uint64_t axis = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axis, "axis"));
}
std::string name() const { return "flatten"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1);
auto&& lens = inputs.front().lens();
if(axis > lens.size())
{
MIGRAPHX_THROW("axis for flatten must be less than tensor rank");
}
auto x =
std::accumulate(lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<>{});
auto y =
std::accumulate(lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
return {inputs.at(0).type(), {x, y}};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
/// The broadcast operator performs the numpy-style broadcasting of an axis of a given tensor. This
/// is achieved primarily by setting the stride of the broadcasted axis to zero. Linear indicies are
/// computed from multi-indicies by computing the inner product on the multi-index with the strides.
/// For example, if we have a tensor A(2,3) it has lengths of (2,3) and strides of (3,1). If we want
/// to compute the linear offset that corresponds to the element on the 2nd row (i = 1) and 3rd
/// column (j = 2), we compute the following inner product (1,2) dot (3, 1) = 1*3 + 2*1 = 5. It is
/// obvious from there that we can negate the effects of a given axis by setting the stride of that
/// axis to zero.
struct broadcast
{
uint64_t axis = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axis, "axis"));
}
shape broadcast_shape;
std::string name() const { return "broadcast"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto t = inputs.at(0).type();
auto input = inputs.at(0);
std::vector<size_t> bcast_strides(broadcast_shape.lens().size(), 0);
if(std::all_of(broadcast_shape.lens().cbegin(), broadcast_shape.lens().cend(), [&](auto x) {
return x == 1;
}))
{
if(axis != 0)
MIGRAPHX_THROW("when broadcasting tensor of size 1, axis should be 0");
return {t, broadcast_shape.lens(), std::move(bcast_strides)};
}
else
{
assert(broadcast_shape.lens().size() - axis >= input.lens().size());
if(!std::equal(
input.lens().begin(), input.lens().end(), broadcast_shape.lens().begin() + axis))
MIGRAPHX_THROW("when broadcasting success sizes must match");
std::copy(input.strides().begin(), input.strides().end(), bcast_strides.begin() + axis);
return {t, broadcast_shape.lens(), std::move(bcast_strides)};
}
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct multibroadcast
{
std::vector<std::size_t> output_lens;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.output_lens, "output_lens"));
}
std::string name() const { return "multibroadcast"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto t = inputs.at(0).type();
auto input = inputs.at(0);
if(input.lens().empty())
MIGRAPHX_THROW("inputs dimensions should be > 0");
if(input.lens().size() > output_lens.size())
MIGRAPHX_THROW("inputs dimensions should <= output size");
std::vector<size_t> bcast_strides(output_lens.size(), 0);
auto offset = output_lens.size() - input.lens().size();
for(int i = input.lens().size() - 1; i >= 0; i--)
{
if(output_lens[i + offset] == input.lens()[i])
{
bcast_strides[i + offset] = input.strides()[i];
}
}
return {t, output_lens, bcast_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct scalar
{
shape scalar_bcast;
std::string name() const { return "scalar"; }
shape compute_shape(std::vector<shape> inputs) const
{
assert(check_shapes{inputs}.has(1).only_dims(1).size() == 1);
auto t = inputs.at(0).type();
std::vector<std::size_t> strides(scalar_bcast.lens().size(), 0);
return {t, scalar_bcast.lens(), strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
template <class Derived>
struct binary
{
std::string name() const
{
static const std::string& name = get_type_name<Derived>();
return name.substr(name.rfind("::") + 2);
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(2).same_type().same_dims();
auto t = inputs.at(0).type();
auto lens = inputs.at(0).lens();
return {t, lens};
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
if(input1.get_shape().standard() and input2.get_shape().standard())
{
std::transform(input1.begin(),
input1.end(),
input2.begin(),
output.begin(),
static_cast<const Derived&>(*this).apply());
}
else
{
shape_for_each(output.get_shape(), [&](const auto& idx) {
output(idx.begin(), idx.end()) = static_cast<const Derived&>(*this).apply()(
input1(idx.begin(), idx.end()), input2(idx.begin(), idx.end()));
});
}
});
return result;
}
};
struct add : binary<add>
{
auto apply() const
{
return [](auto x, auto y) { return x + y; };
}
};
struct sub : binary<sub>
{
auto apply() const
{
return [](auto x, auto y) { return x - y; };
}
};
struct mul : binary<mul>
{
auto apply() const
{
return [](auto x, auto y) { return x * y; };
}
};
struct div : binary<div>
{
auto apply() const
{
return [](auto x, auto y) { return x / y; };
}
};
struct max : binary<max>
{
auto apply() const
{
return [](auto x, auto y) { return std::max(x, y); };
}
};
struct min : binary<min>
{
auto apply() const
{
return [](auto x, auto y) { return std::min(x, y); };
}
};
struct load
{
shape s;
std::size_t offset = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.s, "shape"), f(self.offset, "offset"));
}
std::string name() const { return "load"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs}.has(1);
return s;
}
argument compute(const shape&, const std::vector<argument>& args) const
{
if((offset + s.bytes()) > args[0].get_shape().bytes())
MIGRAPHX_THROW("Load access is out of bounds");
return {s, args[0].data() + offset};
}
int output_alias(const std::vector<shape>&) const { return 0; }
friend std::ostream& operator<<(std::ostream& os, const load& op)
{
os << op.name() << "[";
os << "offset=" << op.offset << ",";
os << "end=" << (op.offset + op.s.bytes()) << "]";
return os;
}
};
struct outline
{
shape s;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.s, "shape"));
}
std::string name() const { return "outline"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(0);
return s;
}
argument compute(const shape&, const std::vector<argument>&) const { return {s, nullptr}; }
};
// indicate rnn computation direction
enum class rnn_direction
{
forward,
reverse,
bidirectional,
};
struct rnn
{
std::size_t hidden_size = 1;
std::vector<operation> actv_funcs{tanh{}, tanh{}};
rnn_direction direction = rnn_direction::forward;
float clip = 0.0f;
std::string name() const { return "rnn"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto in_dims = inputs[0].lens();
auto hidden_dims = inputs[2].lens();
if(hidden_size != hidden_dims[2])
{
MIGRAPHX_THROW("RNN: hidden size mismatch in attribute and input");
}
std::size_t num_directions = 1;
if(direction == rnn_direction::bidirectional)
{
num_directions = 2;
}
if(num_directions != hidden_dims[0])
{
MIGRAPHX_THROW("RNN: num_direction mismatch in attribute and input");
}
std::vector<std::size_t> out_dims(in_dims);
out_dims.insert(out_dims.begin() + 1, num_directions);
out_dims.back() = hidden_size;
return {inputs[0].type(), out_dims};
}
};
struct rnn_last_output
{
std::string name() const { return "rnn_last_output"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto dims = inputs[0].lens();
// remove the first dimension, remaing are output shape
dims.erase(dims.begin());
return {inputs[0].type(), dims};
}
};
struct gru
{
std::size_t hidden_size = 1;
std::vector<operation> actv_funcs{sigmoid{}, tanh{}};
rnn_direction direction = rnn_direction::forward;
float clip = 0.0f;
int linear_before_reset = 0;
std::string name() const { return "gru"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto in_dims = inputs[0].lens();
auto hidden_dims = inputs[2].lens();
if(hidden_size != hidden_dims[2])
{
MIGRAPHX_THROW("GRU: hidden size mismatch in attribute and input");
}
std::size_t num_directions = 1;
if(direction == rnn_direction::bidirectional)
{
num_directions = 2;
}
if(num_directions != hidden_dims[0])
{
MIGRAPHX_THROW("GRU: num_direction does not match the direction attribute");
}
std::vector<std::size_t> out_dims(in_dims);
out_dims.insert(out_dims.begin() + 1, num_directions);
out_dims.back() = hidden_size;
return {inputs[0].type(), out_dims};
}
};
struct lstm
{
std::size_t hidden_size = 1;
std::vector<operation> actv_funcs{sigmoid{}, tanh{}, tanh{}};
rnn_direction direction = rnn_direction::forward;
float clip = 0.0f;
int input_forget = 0;
std::string name() const { return "lstm"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto in_dims = inputs[0].lens();
auto hidden_dims = inputs[2].lens();
if(hidden_size != hidden_dims[2])
{
MIGRAPHX_THROW("LSTM: hidden size mismatch in attribute and input");
}
std::size_t num_directions = 1;
if(direction == rnn_direction::bidirectional)
{
num_directions = 2;
}
if(num_directions != hidden_dims[0])
{
MIGRAPHX_THROW("LSTM: num_direction does not match the direction attribute");
}
std::vector<std::size_t> out_dims(in_dims);
out_dims.insert(out_dims.begin() + 1, num_directions);
out_dims.back() = hidden_size;
return {inputs[0].type(), out_dims};
}
};
struct lstm_last_cell_output
{
std::string name() const { return "lstm_last_cell_output"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto dims = inputs[0].lens();
// remove the first dimension, remaing are output shape
dims.erase(dims.begin());
return {inputs[0].type(), dims};
}
};
struct undefined
{
std::string name() const { return "undefined"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(0);
return {};
}
argument compute(const shape&, const std::vector<argument>&) const { return {{}, nullptr}; }
};
struct unknown
{
std::string op;
std::string name() const { return "unknown:" + op; }
shape compute_shape(std::vector<shape> input) const
{
if(input.empty())
return {};
else
return input.front();
}
friend std::ostream& operator<<(std::ostream& os, const unknown& x)
{
os << x.name();
return os;
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#include <migraphx/op/abnormal_ops.hpp>
#include <migraphx/op/abs.hpp>
#include <migraphx/op/acos.hpp>
#include <migraphx/op/add.hpp>
#include <migraphx/op/asin.hpp>
#include <migraphx/op/as_shape.hpp>
#include <migraphx/op/atan.hpp>
#include <migraphx/op/batch_norm.hpp>
#include <migraphx/op/binary.hpp>
#include <migraphx/op/broadcast.hpp>
#include <migraphx/op/common.hpp>
#include <migraphx/op/concat.hpp>
#include <migraphx/op/contiguous.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/cosh.hpp>
#include <migraphx/op/cos.hpp>
#include <migraphx/op/div.hpp>
#include <migraphx/op/dot.hpp>
#include <migraphx/op/elu.hpp>
#include <migraphx/op/exp.hpp>
#include <migraphx/op/flatten.hpp>
#include <migraphx/op/gather.hpp>
#include <migraphx/op/gru.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/op/im2col.hpp>
#include <migraphx/op/leaky_relu.hpp>
#include <migraphx/op/load.hpp>
#include <migraphx/op/log.hpp>
#include <migraphx/op/logsoftmax.hpp>
#include <migraphx/op/lrn.hpp>
#include <migraphx/op/lstm.hpp>
#include <migraphx/op/max.hpp>
#include <migraphx/op/min.hpp>
#include <migraphx/op/mul.hpp>
#include <migraphx/op/multibroadcast.hpp>
#include <migraphx/op/neg.hpp>
#include <migraphx/op/outline.hpp>
#include <migraphx/op/pad.hpp>
#include <migraphx/op/pooling.hpp>
#include <migraphx/op/relu.hpp>
#include <migraphx/op/reshape.hpp>
#include <migraphx/op/rnn.hpp>
#include <migraphx/op/rnn_last_cell_output.hpp>
#include <migraphx/op/rnn_last_output.hpp>
#include <migraphx/op/scalar.hpp>
#include <migraphx/op/sigmoid.hpp>
#include <migraphx/op/sinh.hpp>
#include <migraphx/op/sin.hpp>
#include <migraphx/op/slice.hpp>
#include <migraphx/op/softmax.hpp>
#include <migraphx/op/squeeze.hpp>
#include <migraphx/op/sub.hpp>
#include <migraphx/op/tanh.hpp>
#include <migraphx/op/tan.hpp>
#include <migraphx/op/transpose.hpp>
#include <migraphx/op/unary.hpp>
#include <migraphx/op/unsqueeze.hpp>
#endif
src/include/migraphx/rewrite_rnn.hpp
View file @ 4b7a267a
......@@ -4,7 +4,7 @@
#include <string>
#include <vector>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/config.hpp>
namespace migraphx {
......
src/onnx/onnx.cpp
View file @ 4b7a267a
......@@ -36,7 +36,6 @@ struct onnx_parser
onnx_parser()
{
add_generic_op("MatMul", op::dot{});
add_generic_op("Relu", op::relu{});
add_generic_op("Sigmoid", op::sigmoid{});
add_generic_op("Abs", op::abs{});
......@@ -77,6 +76,7 @@ struct onnx_parser
add_mem_op("Reshape", &onnx_parser::parse_reshape);
add_mem_op("Flatten", &onnx_parser::parse_flatten);
add_mem_op("Gemm", &onnx_parser::parse_gemm);
add_mem_op("MatMul", &onnx_parser::parse_matmul);
add_mem_op("BatchNormalization", &onnx_parser::parse_batchnorm);
add_mem_op("Softmax", &onnx_parser::parse_softmax);
add_mem_op("LogSoftmax", &onnx_parser::parse_logsoftmax);
......@@ -154,42 +154,48 @@ struct onnx_parser
});
}
std::vector<std::size_t> compute_broadcasted_lens(std::vector<std::size_t> s0,
std::vector<std::size_t> s1)
{
// Example:
// s0 = (3,2,4,5) and s1 = (2,1,1)
//
// In this case we need to broadcast (:,1,1) portion of
// s1 plus broadcast the 1st dimension of s1
// giving output_lens = (3,2,4,5)
//
// Another example:
// s0 = (3,2,1,5) and s1 = (2,7,5)
// In this case we need to broadcast the (:,:,1:,:) axis
// of s0 plus the 1st dimension of s1 giving
// output_lens = (3,2,7,5)
if(s0.size() > s1.size())
{
s0.swap(s1);
}
std::vector<std::size_t> out_lens(s1);
auto offset = s1.size() - s0.size();
std::transform(s0.begin(),
s0.end(),
s1.begin() + offset,
out_lens.begin() + offset,
[](auto a, auto b) { return std::max(a, b); });
return out_lens;
}
template <class T>
instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
{
if(arg0->get_shape().lens() != arg1->get_shape().lens())
{
// Example:
// s0 = (3,2,4,5) and s1 = (2,1,1)
//
// In this case we need to broadcast (:,1,1) portion of
// s1 plus broadcast the 1st dimension of s1
// giving output_lens = (3,2,4,5)
//
// Another example:
// s0 = (3,2,1,5) and s1 = (2,7,5)
// In this case we need to broadcast the (:,:,1:,:) axis
// of s0 plus the 1st dimension of s1 giving
// output_lens = (3,2,7,5)
//
// Get lengths for both arguments
const std::vector<std::size_t>* s0 = &arg0->get_shape().lens();
const std::vector<std::size_t>* s1 = &arg1->get_shape().lens();
// Make sure s0 is the smaller size
if(s0->size() > s1->size())
std::swap(s0, s1);
std::vector<std::size_t> output_lens(*s1);
auto offset = s1->size() - s0->size();
std::transform(s0->begin(),
s0->end(),
s1->begin() + offset,
output_lens.begin() + offset,
[](auto a, auto b) { return std::max(a, b); });
auto l0 = prog.add_instruction(op::multibroadcast{output_lens}, arg0);
auto l1 = prog.add_instruction(op::multibroadcast{output_lens}, arg1);
auto s0 = arg0->get_shape().lens();
auto s1 = arg1->get_shape().lens();
auto out_lens = compute_broadcasted_lens(s0, s1);
auto l0 = prog.add_instruction(op::multibroadcast{out_lens}, arg0);
auto l1 = prog.add_instruction(op::multibroadcast{out_lens}, arg1);
return prog.add_instruction(x, l0, l1);
}
else
......@@ -495,25 +501,86 @@ struct onnx_parser
auto l2 = (transb) ? prog.add_instruction(op::transpose{perm}, args[1]) : args[1];
if(args.size() == 3)
{
if(beta != 0.f)
if(beta != 0.f && args[2]->get_shape().elements() > 0)
{
auto l3 = prog.add_instruction(op::dot{alpha}, l1, l2);
auto l4 = args[2];
if(l4->get_shape().scalar()) // ignore args[2] (no C value added to alpha*A*B)
return l3;
if(beta != 1.f)
auto out_lens = l1->get_shape().lens();
out_lens.back() = l2->get_shape().lens().back();
auto l3 = args[2];
auto l3_lens = l3->get_shape().lens();
if(!std::equal(out_lens.begin(), out_lens.end(), l3_lens.begin(), l3_lens.end()))
{
auto beta_val = prog.add_literal(beta);
auto l5 = prog.add_instruction(op::scalar{args[2]->get_shape()}, beta_val);
l4 = prog.add_instruction(op::mul{}, args[2], l5);
l3 = prog.add_instruction(op::multibroadcast{out_lens}, args[2]);
}
return add_broadcastable_binary_op(l3, l4, op::add{});
return prog.add_instruction(op::dot{alpha, beta}, l1, l2, l3);
}
}
return prog.add_instruction(op::dot{alpha, beta}, l1, l2);
}
instruction_ref
parse_matmul(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
{
auto l0 = args[0];
auto l1 = args[1];
auto l0_lens = l0->get_shape().lens();
auto l1_lens = l1->get_shape().lens();
// args[0] is a vector, prepend 1 to the shape
bool is_a_prepended = false;
if(l0_lens.size() == 1)
{
is_a_prepended = true;
l0_lens.insert(l0_lens.begin(), 1);
l0 = prog.add_instruction(op::unsqueeze{{0}}, args[0]);
}
bool is_b_appended = false;
if(l1_lens.size() == 1)
{
is_b_appended = true;
l1_lens.push_back(1);
l1 = prog.add_instruction(op::unsqueeze{{1}}, args[1]);
}
instruction_ref bl0 = l0;
instruction_ref bl1 = l1;
if(!std::equal(l0_lens.rbegin() + 2, l0_lens.rend(), l1_lens.rbegin() + 2, l1_lens.rend()))
{
auto l0_it = l0_lens.begin() + l0_lens.size() - 2;
std::vector<std::size_t> l0_broadcasted_lens(l0_lens.begin(), l0_it);
auto l1_it = l1_lens.begin() + l1_lens.size() - 2;
std::vector<std::size_t> l1_broadcasted_lens(l1_lens.begin(), l1_it);
auto output_lens = compute_broadcasted_lens(l0_broadcasted_lens, l1_broadcasted_lens);
l0_broadcasted_lens = output_lens;
l0_broadcasted_lens.insert(l0_broadcasted_lens.end(), l0_it, l0_lens.end());
l1_broadcasted_lens = output_lens;
l1_broadcasted_lens.insert(l1_broadcasted_lens.end(), l1_it, l1_lens.end());
if(l0_lens != l0_broadcasted_lens)
{
bl0 = prog.add_instruction(op::multibroadcast{l0_broadcasted_lens}, l0);
}
if(l1_lens != l1_broadcasted_lens)
{
bl1 = prog.add_instruction(op::multibroadcast{l1_broadcasted_lens}, l1);
}
}
auto dot_res = prog.add_instruction(op::dot{1.0f, 0.0f}, bl0, bl1);
int64_t num_axis = static_cast<int64_t>(dot_res->get_shape().lens().size());
if(is_a_prepended)
{
dot_res = prog.add_instruction(op::squeeze{{num_axis - 2}}, dot_res);
--num_axis;
}
if(is_b_appended)
{
dot_res = prog.add_instruction(op::squeeze{{num_axis - 1}}, dot_res);
}
return dot_res;
}
instruction_ref
parse_batchnorm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
{
......
src/opt/memory_coloring_impl.cpp
View file @ 4b7a267a
#include <migraphx/op/load.hpp>
#include "memory_coloring_impl.hpp"
namespace migraphx {
......
src/opt/memory_coloring_impl.hpp
View file @ 4b7a267a
......@@ -3,7 +3,6 @@
#include <migraphx/program.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/pass_config.hpp>
#include <migraphx/config.hpp>
......
src/program.cpp
View file @ 4b7a267a
#include <migraphx/program.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/target.hpp>
#include <migraphx/env.hpp>
#include <migraphx/ranges.hpp>
......
src/schedule.cpp
View file @ 4b7a267a
#include <migraphx/schedule.hpp>
#include <migraphx/program.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/dfor.hpp>
#include <migraphx/functional.hpp>
......
src/simplify_algebra.cpp
View file @ 4b7a267a
#include <migraphx/simplify_algebra.hpp>
#include <migraphx/program.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/op/add.hpp>
#include <migraphx/matcher.hpp>
#include <migraphx/literal.hpp>
......
src/simplify_reshapes.cpp
View file @ 4b7a267a
#include <migraphx/simplify_reshapes.hpp>
#include <migraphx/program.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/op/as_shape.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/ranges.hpp>
#include <unordered_set>
......
src/targets/cpu/gemm.cpp
View file @ 4b7a267a
......@@ -55,7 +55,13 @@ void migemm_impl(tensor_view<T> cmat,
visit_mat(amat, [&](const auto& a) {
visit_mat(bmat, [&](const auto& b) {
auto c = make_mat(cmat);
c = (a * b) * alpha + beta * c;
c = beta * c;
// This is a simple optimization to avoid
// compute A * B if alpha is 0.0
if(alpha != 0.0)
{
c = c + alpha * a * b;
}
});
});
}
......@@ -95,8 +101,8 @@ void migemm_impl(
{
auto lens = amat.get_shape().lens();
bool batch_mul =
std::accumulate(lens.begin(), lens.end(), std::size_t{1}, std::multiplies<std::size_t>()) ==
(*lens.rbegin()) * (*(lens.rbegin() + 1));
std::accumulate(
lens.rbegin() + 2, lens.rend(), std::size_t{1}, std::multiplies<std::size_t>()) == 1;
if(batch_mul)
{
migemm_impl(cmat, amat, bmat, alpha, beta, is_fast_gemm_type<T>{});
......
src/targets/cpu/lowering.cpp
View file @ 4b7a267a
......@@ -369,12 +369,43 @@ struct cpu_gemm
{
op::dot op;
std::string name() const { return "cpu::dot"; }
shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
shape compute_shape(const std::vector<shape>& inputs) const
{
if(inputs.size() == 3)
{
auto c_shape = inputs.at(2);
check_shapes{{c_shape}}.not_broadcasted();
}
return op.compute_shape(inputs);
}
argument compute(context&, const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
migemm(result, args[0], args[1], op.alpha, op.beta);
// 3 inputs, it is alpha * A * B + beta * C, then
// A and B are matrics, and C is broadcastable to A * B
if(args.size() == 3)
{
// no need to consider the value of args[2]
if(op.beta == 0.0f)
{
result.visit([&](auto output) { std::fill(output.begin(), output.end(), 0); });
}
else
{
visit_all(result, args[2])([&](auto output, auto input) {
std::copy(input.begin(), input.end(), output.begin());
});
}
migemm(result, args[0], args[1], op.alpha, op.beta);
return result;
}
// 2 input arguments
migemm(result, args[0], args[1], op.alpha, 0.0f);
return result;
}
};
......
src/targets/gpu/eliminate_workspace.cpp
View file @ 4b7a267a
......@@ -2,7 +2,6 @@
#include <migraphx/gpu/hip.hpp>
#include <migraphx/program.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/stringutils.hpp>
......
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